Ant weight-lifting algorithm for motion estimation

Every video coding standard includes and requires motion estimation and compensation. The full search algorithm, which provides the best motion estimation, has a very high computation cost. Researchers have developed several algorithms to reduce the cost of computation. However, most of these algori...

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Vydáno v:Iran Journal of Computer Science (Online) Ročník 6; číslo 3; s. 207 - 219
Hlavní autoři: Acharjee, Suvojit, Chaudhuri, Sheli Sinha
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 01.09.2023
Springer Nature B.V
Témata:
Artificial Intelligence
Computation
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Evolutionary algorithms
Hoisting
Image compression
Image segmentation
Mathematics of Computing
Motion simulation
Search algorithms
Software Engineering/Programming and Operating Systems
Theory of Computation
Ant weight lifting
VVC
Motion estimation
Block matching algorithm
Block matching
ISSN:2520-8438, 2520-8446
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Shrnutí:Every video coding standard includes and requires motion estimation and compensation. The full search algorithm, which provides the best motion estimation, has a very high computation cost. Researchers have developed several algorithms to reduce the cost of computation. However, most of these algorithms become trapped in local minima during the search. Population-based evolutionary algorithms are widely used to develop a computationally efficient and cost-effective motion estimation strategy. The most recent effort used the Jaya algorithm to develop a motion estimation process that outperformed the state-of-the-art test zone search algorithm. In this study, a motion estimation algorithm based on the ant weight-lifting approach is proposed. Previously, the ant weight-lifting algorithm was used to solve a variety of problems, such as image segmentation, signal compression, and so on. The ant weight-lifting algorithm's computation cost was reduced by adopting a fitness estimation method that uses nearest-neighbor interpolation and an early termination strategy. Compared to Jaya algorithm-based motion estimation, the proposed algorithm executes up to 3% more quickly and exhibits up to 1.2 dB less distortion.
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ISSN:2520-8438
2520-8446
DOI:10.1007/s42044-022-00134-5